Based On Frequency Domain, Wavelet Transform And Neural Network Color Image Enhancement Algorithm

Many real color images, which are photographed from real scenes, possessing high dynamic range include both dark shadows and bright light sources that are difficult to be perceived. The dynamic range of these images should be compressed (that is real color image enhancement) in order that these images are perceived by humans or rendered more suitable for machine analysis. Because real color image enhancement is widely applied in many domain, the research of real color image enhancement is the important worth of application. Because the color sustainment of images enhanced and the application of illumination-reflection model are confined by MSRCR and real color images do not uaually satisfy the demand of MSRCR, the default of "halos", images covered by mist and the actual scenic details and/or colors obscured are common in real color images enhanced. According to the physical and spectroscopic theory of real color images and the physiological theory of human and human eyes and the phychological theory of human vision, we create a new llumination-reflection model and put forward a series of algorithms based on digital signal processing, wavelet transform, neural networks and etc. The flexibility and validity of these algorithms are good.The paper includes five aspects.1. The algorithm that the saturation of real color image is adjusted is put forward. Because HSV color system can approximately separate the color and the value of real color images, according to the spectral sensitivity of most people vision, the saturation of real color image with dark red in HSV can be adjusted by a Gaussian filter to accommodate the observation of human eyes.2. To improve homomorphic filter:(1) The cut-off frequency range of homomorphic filter is confirmed. Because the illumination determine the information of image enhanced, we can analyse the variation of the illumination and the reflection in order that the cut-off frequency is decided in a reasonable scope. (2) A algorithm of real color image enhanced by multi-scale homomorphic filters in two channels is put forward. The method of multi-scale homomorphic filters is put forward in order to eliminate the contradiction between the images detail sustained and the images high dynamic range compressed. 3. A smoothing conduction function method is put forward to enhance images. Retinex is improved by a smoothing conduction function in order that the flexibility of Retinex is increased. The method is better than Retinex.4. Real color image enhanced based on wavelet transform:(1) A homomorphic decomposition-wavelet transform algorithm is put forward. The illumination and the reflection are separated by homomorphic decomposition. The detail is sustained by wavelet transform. (2) A wavelet-energy enhancement is put forwand. A new illumination-reflection model is described by stationary wavelet transform in order to eliminate the limitation of old illumination-reflection model. A new idea is that image enhancement is a process that the imagery energy is reduced and then amplified. Because real color images enhanced by the method are not restricted, the method is much better than MSRCR in flexibility and effect. (3) Real color image is enhanced and denoised by stationary wavelet transform at one time. We analyse the noise of real color image in HSV system. The noise in the reflection is eliminated by bayes-soft-threshold when real color image is enhanced by wavelet-energy.5. Real color image enhanced based on neural networks:(1) A wavelet-PCNN (pulse coupled neural networks) is put forward. According to Weber theory, the exponents which adjust the imagery value are decided by PCNN. Different parts possessing different luminance in image are amplified by different exponent. The method is called Gamma adjustment in different levels. High bright parts are reduced and shade parts are amplified at the same time in one image. The method is better than the wavelet-energy enhancement. (2) RNNs (recurrent neural networks) revise the color of real color images enhanced. RNNs (recurrent neural networks) possess the recall ability. Based on the analysis of RNN's stability and convergence, weight matrix of recurrent neural networks is properly confirmed to revise the color real color images enhanced in the paper.