Mr. Ilya V. Romanenko Profile

Ilya V. Romanenko

at Apical

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Publications (3)

Proceedings Article | 7 March 2014 Paper

Image matching in Bayer raw domain to de-noise low-light still images, optimized for real-time implementation

I. Romanenko, E. Edirisinghe, D. Larkin

Proceedings Volume 9020, 90200Y (2014) https://doi.org/10.1117/12.2042489

KEYWORDS: Sensors, Denoising, Image processing, Optical flow, Image sensors, Data modeling, Image filtering, Image resolution, Cameras, Signal to noise ratio

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Temporal accumulation of images is a well-known approach to improve signal to noise ratios of still images taken in a low light conditions. However, the complexity of known algorithms often leads to high hardware resource usage, increased memory bandwidth and computational complexity, making their practical use impossible. In our research we attempt to solve this problem with an implementation of a practical spatial-temporal de-noising algorithm, based on image accumulation. Image matching and spatial-temporal filtering was performed in Bayer RAW data space, which allowed us to benefit from predictable sensor noise characteristics, thus allowing using a range of algorithmic optimizations. The proposed algorithm accurately compensates for global and local motion and efficiently removes different kinds of noise in noisy images taken in low light conditions. In our algorithm we were able to perform global and local motion compensation in Bayer RAW data space, while preserving the resolution and effectively improving signal to noise ratios of moving objects as well as non-stationary background. The proposed algorithm is suitable for implementation in commercial grade FPGA’s and capable of processing 16MP images at capturing rate (10 frames per second). The main challenge for matching between still images is the compromise between the quality of the motion prediction and the complexity of the algorithm and required memory bandwidth. Still images taken in a burst sequence must be aligned to compensate for background motion and foreground objects movements in a scene. High resolution still images coupled with significant time between successive frames can produce large displacements between images, which creates additional difficulty for image matching algorithms. In photo applications it is very important that the noise is efficiently removed in both static, and non-static background as well as in a moving objects, maintaining the resolution of the image. In our proposed algorithm we solved the issue of matching current image with accumulated image data in Bayer RAW data space in order to efficiently perform spatio-temporal noise reduction and reduce the computational requirements. In this paper we provide subjective experimental results to demonstrate the ability of the proposed method to match noisy still images in order to perform efficient de-noising and avoid motion artefacts in resulting still images.

Proceedings Article | 19 February 2013 Paper

Spatial-temporal noise reduction method optimized for real-time implementation

I. Romanenko, E. Edirisinghe, D. Larkin

Proceedings Volume 8655, 86550L (2013) https://doi.org/10.1117/12.2001661

KEYWORDS: Sensors, Denoising, Video, Image filtering, Image processing, RGB color model, Image sensors, Data modeling, Video surveillance, Nonlinear filtering

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Proceedings Article | 2 May 2012 Paper

Block matching noise reduction method for photographic images applied in Bayer RAW domain and optimized for real-time implementation

I. Romanenko, E. Edirisinghe, D. Larkin

Proceedings Volume 8437, 84370F (2012) https://doi.org/10.1117/12.922791

KEYWORDS: Image processing, Denoising, Sensors, Image filtering, Video, RGB color model, Signal processing, Video processing, Cameras, Optical filters

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Image de-noising has been a well studied problem in the field of digital image processing. However there are a number of problems, preventing state-of-the-art algorithms finding their way to practical implementations. In our research we have solved these issues with an implementation of a practical de-noising algorithm. In order of importance: firstly we have designed a robust algorithm, tackling different kinds of nose in a very wide range of signal to noise ratios, secondly in our algorithm we tried to achieve natural looking processed images and to avoid unnatural looking artifacts, thirdly we have designed the algorithm to be suitable for implementation in commercial grade FPGA's capable of processing full HD (1920×1080) video data in real time (60 frame per second). The main challenge for the use of noise reduction algorithms in photo and video applications is the compromise between the efficiency of the algorithm (amount of PSNR improvement), loss of details, appearance of artifacts and the complexity of the algorithm (and consequentially the cost of integration). In photo and video applications it is very important that the residual noise and artifacts produced by the noise reduction algorithm should look natural and do not distract aesthetically. Our proposed algorithm does not produce artificially looking defects found in existing state-of-theart algorithms. In our research, we propose a robust and fast non-local de-noising algorithm. The algorithm is based on a Laplacian pyramid. The advantage of this approach is the ability to build noise reduction algorithms with a very large effective kernel. In our experiments effective kernel sizes as big as 127×127 pixels were used in some cases, which only required 4 scales. This size of a kernel was required to perform noise reduction for the images taken with a DSLR camera. Taking into account the achievable improvement in PSNR (on the level of the best known noise reduction techniques) and low algorithmic complexity, enabling its practical use in commercial photo, video applications, the results of our research can be very valuable.

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