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
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.