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mobappdevelopmentcompany · 3 years ago

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Healthcare Mobile App Categories and Best Development Practices!

The digital revolution has gifted the healthcare industry with pioneering healthcare app solutions that have transformed its functioning altogether. Owing to their innumerable advantages, Healthcare Mobile Apps have been widely adopted by practitioners, patients, medical facilities, and insurance providers.

Here are some numbers concerning the market size of these healthcare apps:

“As predicted by Statista, the market size for mHealth applications is going to exceed 50 Billion USD by the year 2025.”

This humongous demand for mHealth apps indicates that the Healthcare Mobile App Development market is booming with opportunities for businesses enterprises.

But, designing a flawless medical app solution that delivers a rich UX is no cakewalk. So, this post is meant for those who want to leverage this opportunity and create a healthcare app solution. The post enlists the key categories of healthcare mobile apps as well as the most effective strategies to implement during the entire app development process right from app ideation to monetization post-launch.

Healthcare Mobile Apps: Key Categories

Healthcare mobile apps come in various categories. The noteworthy ones are as follows.

Medical Record Maintenance apps

This category of mHealth apps come with the following functionalities:

Scheduling doctor-patient appointments

Easing out the process of updating patients’ records such as doctor visits, medical examinations, prescriptions, health parameters like blood pressure/sugar levels, etc. for tracking patients’ progress.

Enabling practitioners to save healthcare info of patients using the prescription module

Recording procedures and allowing searching patients via their I.D.

Risk Assessment Applications

These apps are integrated with wearable devices and EHR (Electronic Health Records) for real-time monitoring of medical conditions- tracking parameters like heart rate, identifying health risks in patients, electronic fetal monitoring for patients in labor (EFM), etc. Healthcare app developers often use machine learning techniques for pattern recognition when designing risk assessment apps.

Doctor-on-demand/Telemedicine Apps

Telemedicine apps commonly known as ��doctor-on-demand” provide patients the convenience of receiving doctor consultations as well as treatment from the comfort of their homes or offices. Patients can choose from a wide range of practitioners, book appointments, attend the consultation session via video conferencing, and make payments using an integrated payment gateway. Some special features of telemedicine apps include:

A two-way video connection between patients and doctors

Virtual waiting rooms that track the patients’ waiting time and allow them to gather the required information prior to consultation

Options that provide patients the feel of attending offline appointments including EHR, in-built messaging, e-Prescriptions, etc.

Healthcare Reference and Database Apps

Such applications keep doctors informed about the most recent healthcare information concerning diseases, medical conditions, symptoms about diseases, medications, etc.

Clinical Communication Apps

These apps facilitate communication with clinical specialists in a healthcare facility and thereby improve the process of clinical decision-making. The features include messaging, voice chatting, file sharing, and maintaining EHR systems. Some examples are sending PACS and Lab results to doctors, EHR integration for editing patients’ records in real-time, and storing files on a cloud-based platform like AWS, Google Cloud, Microsoft Azure, etc.

Lifestyle and Fitness Apps

These apps are a blend of daily tips on healthcare and fitness, diet, nutrition, exercises, and training/courses for improving an individual’s well-being and state of mind. These apps also track sleep patterns and assist in practicing mindfulness.

Healthcare Mobile App Development: Major Strategies

Identify a Unique Selling Point

The mobile app market is flooded with various outstanding solutions. Therefore, the healthcare app you have envisioned wouldn’t taste success unless it has a unique selling point. For this reason, you need to:

Conduct a thorough market analysis of the existing apps to identify the gaps that can be filled and the specific features that will provide additional value to your application.

Analyze your target audience to figure out their pain areas and comprehend their requirements; so that you can develop a customized medical app solution that suits their needs.

Carry out competitor analysis to gauge the minimum benchmark required to succeed and spot the mistakes they have committed.

Having a word with the stakeholders of the healthcare industry such as doctors, patients, nurses, paramedical staff, etc. will provide insights on creating an end-product that is effective, practically handy, and genuinely helpful.

Adopt an Omnichannel Strategy

The following perplexing questions should pop up before you commence the development process.

Which device is most suitable for the app – a smartphone or a tablet?

Which platform to pick – Android, iOS, or both?

Is there any need to activate the app/website with Google Assistant or Siri?

Would in-person interactions utilizing the camera prove more beneficial for the app?

Well, before you decide on the ideal blend of touch-points for your app, you must consider certain factors. We are living in an omnichannel world wherein digital users do not interact with an organization using only one channel, and so adopting a multi-touch-point strategy will work.

However, your touch-point strategy depends upon the preferences of your targeted audience as well. This is because the preferences of users belonging to different age groups are not the same. For instance, elderly consumers mostly prefer either a tablet or a website, whereas the youngsters are likely to interact with your healthcare solution using a mobile app and voice assistants like Google Home Skill or Alexa.

Opt for the MVP-first approach

Another crucial decision is choosing the right approach – “Whether to create a full-fledged app at one go or build an MVP first and then gradually add features through updated versions?” Developing a feature-packed healthcare app may sound like a lucrative option, but for expediting the success of the app, the MVP-first approach is good to go with.

An MVP refers to the most basic version including all the essential features. The MVP-first approach is profitable because it speeds up the time-to-market. Furthermore, you get an opportunity to validate your app idea and can utilize user feedback to add the right set of features incrementally, thereby adding value to your product.

Integrations and APIs

For promoting interoperability, it is necessary to integrate your app with EMRs and EHRs. Additionally, integration with fitness and healthcare wearables and utilization of AI algorithms for obtaining actionable and insightful inferences based on the collected data works wonders for optimizing healthcare outcomes. For this reason, you must ensure whether the corresponding APIs are available for commercial usage and if there is any need to open source your code after you use them.

Adhere to HIPAA and HITECH regulations

For making your healthcare app HIPAA compliant, you need to adhere to the HIPAA Privacy Rule and the HIPAA Security Rule.

HIPAA Privacy Rule sets national standards in the United States for protecting the health records of individuals as well as their personal healthcare information including medical clearinghouses, health plans, etc., and the medical providers conducting healthcare transactions electronically. This rule limits the usage and disclosure of patient information without their authorization, provides patients the right to obtain a copy of their medical data and request corrections, etc.

As per the HIPAA Security Rule, apt technical, physical, and administrative standards have to be maintained for ensuring the security, confidentiality, and integrity of electronic medical data.

HITECH, the Health Information Technology for Economic and Clinical Health Act, mandates the adoption and meaningful utilization of health information technology in mHealth apps. It addresses the safety and privacy concerns related to the electronic submission of medical information and facilitates the civil and criminal enforcement of HIPAA regulations.

Conduct Extensive Testing

Healthcare apps require rigorous testing to ensure the creation of an impeccable end-product. This is because the bar for accuracy, quality, safety, and security is considerably high; and your app must conform to the highest standards of quality assurance before you publish it on the app store. Check out the standardized testing approach all medical app testers need to follow.

To ensure confidentiality and privacy of healthcare data, set up stern authorization and access control measures within the app. This way the data will be delivered to only the intended recipients.

Medical data being a soft target for hackers, testers need to identify and get rid of all areas of vulnerability. So, encrypt data via a transport layer security to enable data validation within the app.

A usability test is crucial as even a minor error can lead to clinical issues and adversely impact the app’s overall outcome.

Test the app against high load and performance benchmarks as ready access to patients’ data has to be provided whenever needed.

Plan App Monetization Strategies

Take a look at the commonest app monetizing strategies.

Offering free trials to users in case of paid apps

Adopting the models of in-app purchases and subscription

Generating revenue via advertisements and medicine sales

Final Verdict:

I hope this post has enlightened you about the key areas you need to watch out for while developing a disruptive healthcare mobile app. Contact us for further queries.

If you have already built a fully functional Healthcare application, let us know your experiences in the comments section below.

To know more about our core technologies, refer to links below:

RoR App Development

Java App Development

Swift App Development

#Healthcare Mobile App Development

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iotagger · 7 years ago

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2017 was the year of numbers: AI, YCombinator and investment funds

AI/Vision recognition:

As we start 2018 lets review some stories that covered AI, an industry that has gained recognition as one of the leading subjects in tech. Last year Intel acquired Mobileye, a vision-based driver-assistance systems that provides warnings for collision prevention and mitigation. Below is a recap on TechCrunch’s story.

Intel buys Mobileye in $15.3B deal, moves its automotive unit to Israel

Intel has confirmed that it is acquiring Mobileye, a leader in computer vision for autonomous driving technology, for $15.3 billion — the biggest-ever acquisition of an Israeli tech company.

Mobileye today covers a range of technology and services, including sensor fusion, mapping, front- and rear-facing camera tech and, beginning in 2018, crowdsourcing data for high-definition maps, as well as driving policy intelligence underlying driving decisions.

Intel has been working officially with Mobileye since last year. Earlier this year, with BMW, the two started to test 40 self-driving cars equipped with the two companies’ technology. Mobileye was also an early partner of Tesla’s for its autonomous technology, although that relationship is ending amid some controversial undertones about safety measures at the carmaker. Other investments that Intel has made in the space of cars include taking a stake in Here (which will feed into the mapping initiatives at Mobileye); acquiring Itseez and Yogitech for safety and navigation functionalities in autonomous cars; making a commitment of at least $250 million to the space (which sounds so tiny considering today’s price tag).

Catch the entire article at: https://techcrunch.com/2017/03/13/reports-intel-buying-mobileye-for-up-to-16b-to-expand-in-self-driving-tech/

Intel Unveils Neural Compute Engine in Movidius Myriad X VPU to Unleash AI at the Edge

Intel introduced its new Movidius Myriad X vision processing unit (VPU), advancing Intel’s end-to-end portfolio of artificial intelligence (AI) solutions to deliver more autonomous capabilities across a wide range of product categories including drones, robotics, smart cameras and virtual reality.

Myriad X is world’s first system-on-chip (SOC) shipping with a dedicated Neural Compute Engine for accelerating deep learning inferences at the edge.

Read Intel’s full disclosure of Movidius at https://newsroom.intel.com/news/intel-unveils-neural-compute-engine-movidius-myriad-x-vpu-unleash-ai-edge/

Investment funds:

There were a handful of announcements of big companies launching their investment funds to further develop technological gaps they might be experiencing.

Samsung is launching a $150M fund to invest in emerging tech startups

The Samsung NEXT Fund is a $150 million venture fund targeted at early-stage startups working in emerging tech.

Specifically, they will be looking for companies focused on virtual reality, artificial intelligence, Internet of Things and “other new frontier technologies.”

So far the fund has invested in 10 startups: Converge Industries, Dashbot, Entry Point VR, Filament, Intezer, LiquidSky, Otto Radio, 2Sens, SafeDK and Virtru. These companies range from a VR animation studio to water monitoring sensors for your appliances — signaling that NEXT will stay pretty broad with its investments.

Catch the entire story at: https://techcrunch.com/2017/01/04/samsung-is-launching-a-150m-fund-to-invest-in-emerging-tech-startups/

Renault-Nissan-Mitsubishi launches $1 billion corporate venture capital fund

Renault–Nissan–Mitsubishi, has launched a $1 billion corporate venture capital fund to focus on investments in “new mobility” including electrification, autonomous systems, network connectivity and artificial intelligence.

Called Alliance Ventures, the fund has already made its first investment, taking an equity stake for an undisclosed amount in Ionic Materials — a Bill Joy-backed battery technology developer.

Catch the entire story at: https://techcrunch.com/2018/01/09/renault-nissan-mitsubishi-launches-1-billion-corporate-venture-capital-fund/

The Startup world:

I was amazed by all the great ideas behind the selected 52 Y Combinator 2017 startups. Below are a few I found intriguing, the full list available at https://techcrunch.com/2017/03/20/yc-demo-day-winter-2017/

52 startups that launched at Y Combinator W17 Demo Day 1

Cambridge Bio-Augmentation Systems – Plug-and-play standard for human bionics

CBAS wants to be the USB port for the human body. The startup has developed a low-cost implant that can connect any bionic device, like artificial limbs, to any part of the body, and give patients control of their prostheses. It also created the first live streaming nerve implant to gather data from a pig’s leg. CBAS wants to be the standard on which all bionic implants build. Just for amputees, this could be a $9 billion a year market, and now 10 companies have switched to the CBAS standard.

Cowlar – Fitbit for cows

It’s way smarter than it sounds. Cowlar makes a special collar for cows that tracks their temperature, activity and other data, and makes it available to farmers. It can recognize a cow with an infected hoof from the change in its gait, or identify that a cow is pregnant and will eventually produce milk. Cowlar costs $69 per cow with a $3 per month subscription, and provides a solar-powered “cow router” that collects data from the collars. It already has 600 cows on the platform, with 7,200 on the waitlist, and has alerted farmers about 103 sick cows. It already has the opportunity to build an $11 billion business on milk cows, and could enter the 3X-bigger beef cow market next. While the idea might seem ridiculous, cows are a huge part of the economy, and more data on them leads to more revenue from them.

Volans-i – B2B drone delivery

Amazon promised consumer drone delivery via Amazon PrimeAir but you likely don’t need a toothbrush in your backyard via drone. Businesses, however, do need deliveries fast, and volans-i aims to speedily deliver those shipments door-to-door via drone. For example, a company may need a certain part delivered to keep up with production. Volans-i steers its drones to this company with up to 50 lbs of payload going 1,000 miles — or 15,000 shipments per year per customer, according to the company. Volans-i’s team comes from strong industry backgrounds including Northrup Grumman, and the company believes it can save time and money for a number of business categories, including high-tech, aviation, medical, oil and gas, automotive and manufacturing.

Delee – A blood test for circulating tumor cells

Cancer is one of the biggest killers in the U.S. and Delee aims to take on the “emperor of all maladies” with a blood test device able to isolate tumor cells. The patent-pending device aims to personalize cancer treatments based on a new approach.The founding team comes from Stanford and the Rochester Institute of Technology and works in biomedical engineering; the startup has already run a successful clinical trial using 15 patients, isolating tumor cells for 13 of them. Delee aims to take on bigger companies working in the same space, like Grail, and take on a $2 billion market.

Indee – Hardware for gene delivery

Indee provides a new way to use CRISPR to edit the genome of cells. Current methods of editing genes include using viruses or electroporation, but Indee uses microturbulence to dissolve molecules within a cell. It currently is targeting a $1.5 billion addressable market for immunotherapy, but sees that market growing to $9 billion as the market improves.

#startup #ai #artifical intelligence #drones #tech

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alexianewsletter · 5 years ago

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bigyack-com · 5 years ago

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Boeing Hoping for 737 MAX Ungrounding in Mid-2020

Boeing has told customers and suppliers that it now hopes the ungrounding of the 737 MAX will begin mid-2020. The Boeing 737 MAX is subject to ongoing attempts to address known schedule risks and further developments that may arise in connection with the certification processes being carried out. It also accounts for the rigorous scrutiny that regulatory authorities are quite rightly applying at every step of their review of the 737 MAX's flight control system and the Joint Operations Evaluation Board process which determines pilot training requirements. Airlines will now have to assess the news. Air Canada, for example, has removed the Boeing 737 MAX from its operating schedule until 30 June 2020. Boeing has said that it will provide additional information about its efforts to safely return the 737 MAX to service in connection with its quarterly financial disclosures next week. See latest Travel News, Interviews, Podcasts and other news regarding: Boeing, 737, MAX. Headlines: Pictures from Ascot United vs Hanworth Villa SHOW DC Hall to Give Bangkok's MICE Industry a Significant Boost IHG Signs First voco Hotel in New Zealand OYO Signs Global Distribution Agreement with Sabre Hong Kong Cancels Chinese New Year Carnival NAC Orders 20 Airbus A220 Aircraft IHG to Launch Customer Insights Portal for Large Enterprises Cathay Pacific Reports December 2019 Traffic Duetto Appoints David Woolenberg as CEO Phuket to Host Thailand Travel Mart (TTM+) 2020 in June Sabre and Accor to Create Unified Technology Platform for Hospitality Industry Groupe Couleur to Manage RWC 2023 Official Travel Agent Selection Process Two Senior Global Marketing Appointments at IHG ANA Begins Autonomous Electric Bus Trial at Haneda Airport China Airlines and Mandarin Airlines Cancel Flights to Wuhan, China Rosa Wong Joins Hotel Alexandra HK as Director of Event Management Four Seasons Madrid Now Accepting Reservations UK Military Flight Training System Orders 4 More H145 Helicopters Sabre Forges 10-Year Partnership with Google Accor Expands Pullman Brand to Rotorua, New Zealand Korean Air Reveals More Details of New SkyPass FFP Airbus to Produce A321 Aircraft in Toulouse, France Cornelia Mitlmeier Joins Four Seasons Dubai as Resort Manager Hotel Alexandra Appoints Daniel Chan as Executive Chef SITAOnAir Acquires GTD Air Services Boeing Hoping for 737 MAX Ungrounding in Mid-2020 1.5 Billion Int. Tourist Arrivals in 2019; UNWTO Forecasts 4% Increase in 2020 Hong Kong Airport Handled 71.5 Million Passengers in 2019 Ascott Opens First Citadines in Osaka, Japan Thai Airways Appoints New Chairman Singapore Airshow Aviation Leadership Summit to Take Place 9-10 Feb PATA Forecasts Over 971 Million Int. 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pauldeckerus · 6 years ago

Text

Can JPEGs Be Improved to RAW Quality?

Over the years, I have come across this question often: “Can a JPEG image be enhanced to a RAW image quality?” I understand where this question comes from. Photographers are taught to “shoot in RAW” from day one. These days, so many images are only in JPEG format, from phone photos to stock photos, to those priceless old pictures. It would be really nice if we could enhance them to RAW image quality so they could fit into today’s photographer’s RAW image editing workflow!

Full disclosure: This article was sponsored by Topaz Labs, the photographer’s editing toolbox.

Unfortunately, the answer has always been “No.” JPEG format was introduced in 1994 mainly to reduce file size and conserve memory space. Images saved as JPEG are a lot smaller in file size and have also lost a lot of information. From the information theory of point of view, that lost data is unrecoverable and the damage is irreversible. There are changes you can make to the image to make it look a little better, as we did with our Photoshop plugin “Topaz DeJPEG”, which used an algorithm to remove blocky artifacts in near “optimal” sense (optimal Mean Squared Error). However, even DeJPEG simply cannot recover the lost details.

Figure 1. Guess which pictures is JPEG, which is RAW, or something else?

However, I recently became hopeful that my “No” could eventually become a “Yes.” Before I explain further, let’s dig a little deeper into the difference between a JPEG and a RAW image.

RAW images emerged when high-quality DLSR digital cameras started to replace film cameras. A RAW image usually comes directly from a DLSR camera by saving imaging sensor data with minimum alteration so that maximum image quality can be achieved during the post editing. This is possible because DSLR cameras use the best image sensors and high-quality optics, resulting in RAW images that are high quality.

Optics/Lens → Imaging Sensor → 10-14bit per RGB → save to RAW file in 10-14 bits per

JPEG images typically come from small cameras, like your phone, or are saved after editing to reduce image size for the internet. Phone or low-cost point-and-shoot cameras have very small image sensors (the red one in figure 2 is an iPhone imager size, as opposed to DLSR full frame), which will produce much higher noise. In-camera pre-processing has to be used to suppress the noise to make the image looks good. This noise suppression also removes fine details in the images. Then the images are converted to 8-bit sRGB colorspace to further reduce the amount of data in the image. This is a shame because sRGB can only represent about 50% of the colors that your eye can see, as illustrated in Figure 3. Finally, the JPEG encoder compresses the image into an even smaller file size which results in more loss of information.

lens/sensor → pre-processing → (8bit sRGB) → JPEG encoder → JPEG file

Figure 2, camera imaging sensor size

Figure 3, colorspace diagram

To summarize the difference between JPEG and RAW:

Don’t get me wrong. When a JPEG image is saved with “Quality > 70”, it “looks” almost as good as a RAW image. The problems only manifest when you edit the image in a strong way.

With the information losses in JPEG’s encoding and the fact that jpeg cameras tend to have small lenses and sensors, it is easy to conclude that it is impossible to enhance JPEG images into raw quality… well, until recently, that is.

In the last few years, there has been some revolutionary development in Artificial Intelligence and its application in image enhancement. Using “deep learning”, artificial neural networks have achieved some amazing results (see our AI Gigapixel and AI Clear). With these developments, it was natural for us to try to use A.I. to enhance a JPEG image to (hopefully) RAW image quality!

But wait, I just said that it is theoretically impossible to recover the lost information. How can an artificial neural network do the impossible?

The easiest way to explain it is that A.I. approaches this problem from a very different angle. Instead of trying to reverse the information loss, which is impossible, we train a neural network to “remember” what the RAW image should look like before it’s converted to a JPEG image. We train the neural network on a large number of high-quality RAW images together with their corresponding JPEG version so that it learns and remembers the correspondence. After training the network on enough images, the neural network will “remember” to output a higher quality image even when given a new “unseen” JPEG image as input. It’s just like when you see a friend from far away. Though they are too far away for you to make out the details with your eyes, you can imagine or remember what your friend looks like in vivid detail.

Figure 4. This is what the neural network in our brain looks like.

Figure 5. This is what the artificial neural network looks like.

To make this approach work, we faced the following challenges:

RAW image dataset for training. We needed raw images of all subjects and types – lots of them. We searched the internet, we begged people to allow our machine to see their RAW images, we dug out all of our vacation photos, we shot more. Eventually, we built the most comprehensive RAW image dataset that I know of in deep learning fields.

A proper neural network architecture and the method to train it. For this, we took advantage of our previous research in image upsampling (Topaz A.I. Gigapixel) and noise reduction (Topaz A.I. Clear) and used our proven neural network architecture. We went above and beyond though and made it even deeper – over 30 layers of neurons. We also invested in large GPU servers and ran them for months at a time to let the network slowly learn from our large RAW image dataset.

Run the deep neural network fast enough on a PC. We continued to refine our own proprietary GPU based neural network inference engine to allow it to run acceptably on at least mid-end personal computers.

After months of training and a lot of late nights for our team, we are proud to introduce our new software “Topaz JPEG to RAW AI.” Look back at Figure 1: the right image is a high-quality RAW image, the left image is JPEG compressed with “quality=15”, the middle one is the JPEG after being enhanced by “Topaz JPEG to RAW AI”. This is an extreme case and the output is not quite “RAW” image quality, but the A.I. does a pretty impressive job given the JPEG is extremely compressed and has lost almost all details.

Overall, my scorecard for our current attempt to use AI to enhance JPEG is summarized below:

Here are a few more images to compare.

Remove compression artifacts:

Recover details and sharpen:

Remove noise:

Are the enhanced JPEGs now RAW quality? That’s for you to judge. We will continue to work on improving it. We encourage you to try JPEG to RAW AI on your own images by downloading the free trial. Let us know what you think – we’re always open to feedback.

At Topaz Labs, we are so excited about the possibilities of A.I. for photo processing. We are just starting to scratch the surface of A.I.’s potential for photography. In due time, we believe this technology will bring many great tools to assist artists and photographers in their creative pursuit.

Disclaimer: New JPEG standard does allow you to save the image in higher quality, e.g. encoded in 16-bit. However, my discussion above applies to the majority (I suspect > 95%) of the existing JPEG images. Example images used are pretty extreme cases to illustrate the points. Most JPEG images you have probably are not that bad! :-)

About the author: Dr. Yang founded Topaz Labs 12 years ago due to his life-long passion for signal and image enhancement. His knowledge and skills have been instrumental in leading Topaz Labs to be an industry frontrunner in using new technology in their photography products – from innovative masking (Topaz ReMask), best quality photo noise reduction (Topaz Denoise), to Artificial Intelligence-based products, such as A.I. Gigapixel and A.I. Clear. Dr. Yang is also a long-time entrepreneur, co-founder and CTO of Techwell Inc. (video enhancement semiconductors, went IPO in 2006) and the co-founder and CTO of Fortemedia Inc (small microphone array technology for voice enhancement). Dr. Yang received his Ph.D in System Design Engineering, University of Waterloo, Canada.

from Photography News https://petapixel.com/2019/01/29/can-jpegs-be-improved-to-raw-quality/

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sailorrrvenus · 6 years ago

Text

Can JPEGs Be Improved to RAW Quality?

Full disclosure: This article was sponsored by Topaz Labs, the photographer’s editing toolbox.

Figure 1. Guess which pictures is JPEG, which is RAW, or something else?

However, I recently became hopeful that my “No” could eventually become a “Yes.” Before I explain further, let’s dig a little deeper into the difference between a JPEG and a RAW image.

Optics/Lens → Imaging Sensor → 10-14bit per RGB → save to RAW file in 10-14 bits per

lens/sensor → pre-processing → (8bit sRGB) → JPEG encoder → JPEG file

Figure 2, camera imaging sensor size

Figure 3, colorspace diagram

To summarize the difference between JPEG and RAW:

Don’t get me wrong. When a JPEG image is saved with “Quality > 70”, it “looks” almost as good as a RAW image. The problems only manifest when you edit the image in a strong way.

But wait, I just said that it is theoretically impossible to recover the lost information. How can an artificial neural network do the impossible?

Figure 4. This is what the neural network in our brain looks like.

Figure 5. This is what the artificial neural network looks like.

To make this approach work, we faced the following challenges:

Overall, my scorecard for our current attempt to use AI to enhance JPEG is summarized below:

Here are a few more images to compare.

Remove compression artifacts:

Recover details and sharpen:

Remove noise:

source https://petapixel.com/2019/01/29/can-jpegs-be-improved-to-raw-quality/

0 notes