Henry Rantanen

Color video signal processing with median filters

Methods of applying median filtering for three-dimensional video signals were studied and are compared. Median operation and RGB- versus YUV-domain processing are discussed. An introduction to various ordering methods in median-based multidimensional filtering is given. The various 3-D median processing methods and the effect of the conversion from the RGB to the YUV domain were tested. The signal processing methods used for testing were noise reduction and interlaced-to-progressive conversion. The test results are given. >

Noise reduction techniques for Bayer-matrix images

In this paper, some arrangements to apply Noise Reduction (NR) techniques for images captured by a single sensor digital camera are studied. Usually, the NR filter processes full three-color component image data. This requires that raw Bayer-matrix image data, available from the image sensor, is first interpolated by using Color Filter Array Interpolation (CFAI) method. Another choice is that the raw Bayer-matrix image data is processed directly. The advantages and disadvantages of both processing orders, before (pre-) CFAI and after (post-) CFAI, are studied with linear, multi-stage median, multistage median hybrid and median-rational filters .The comparison is based on the quality of the output image, the processing power requirements and the amount of memory needed. Also the solution, which improves preservation of details in the NR filtering before the CFAI, is proposed.

Evaluation of scanning rate up conversion algorithms; subjective testing of interlaced to progressive conversion

The suitability of different scanning formats and upconversion algorithms for improved definition television (IDTV) applications is evaluated. The properties of different scanning formats are discussed. State-of-the-art interlaced-to-progressive conversion algorithms are introduced. Algorithms were evaluated with subjective testing methods, and the results are compared with objective measures. >

Sharpening methods for images captured through Bayer matrix

Image resolution and sharpness are essential criteria for a human observer when estimating the image quality. Typically cheap small-sized, low-resolution CMOS-camera sensors do not provide sharp enough images, at least when comparing to high-end digital cameras. Sharpening function can be used to increase the subjective sharpness seen by the observer. In this paper, few methods to apply sharpening for images captured by CMOS imaging sensors through color filter array (CFA) are compared. The sharpening easily adds also the visibility of noise, pixel-cross talk and interpolation artifacts. Necessary arrangements to avoid the amplification of these unwanted phenomenon are discussed. By applying the sharpening only to the green component the processing power requirements can be clearly reduced. By adjusting the red and blue component sharpness, according to the green component sharpening, creation of false colors are reduced highly. Direction search sharpening method can be used to reduce the amplification of the artifacts caused by the CFA interpolation (CFAI). The comparison of the presented methods is based mainly on subjective image quality. Also the processing power and memory requirements are considered.

Prototyping IDTV functions using PGAs (programmable gate arrays)

The utilization of user-programmable gate arrays (PGAs) to build a TV prototype is discussed. Two IDTV prototypes have been constructed in which some image processing algorithms (e.g. interpolation, motion detection, and video signal filtering) have been implemented by PGAs. The PGAs have enabled fast prototype building and testing of various algorithms on the same printed circuit board. Only a memory device must be reprogrammed to change the action of the PGA chip. After algorithms have been implemented and tested by PGAs, they are easily implementable by ASIC (application-specific integrated circuits) technology. >