In the process of the image generation, because the imaging system itself has differences in terms of nonlinear or cameraman perspective, the generated image will face the geometric distortion. Image distortion in general is also a kind of image degradation, which needs the geometric transform to correct each pixel position of the distorted images, so as to regain the original spatial relationships between pixels and the original grey value relation, and which is also one of important steps of image processing. From the point of view of the digital image processing, the distortion correction is actually a process of image restoration for a degraded image. In image processing, in terms of the image quality improvement and correction technology, namely the image restoration, with the wide expansion of digital image distortion correction processing applied, the processing technology of the image restoration has also become a research hotspot. In view of the image distortion issue, this paper puts forward the image distortion correction algorithm based on two-step and one-dimensional linear gray level interpolation to reduce the computation complexity of the bilinear interpolation method, and divide the distorted image into multiple quadrilaterals, and the area of the quadrilateral is associated with the distortion degree of the image in the given region, and express the region distortion of each quadrilateral with the bilinear model, thus determining parameters of bilinear model according to the position of the quadrilateral vertex in the target image and the distorted image. Experiments show that such algorithm in this paper can meet the requirements of distortion correction of most lenses, which can accurately extract the distorted edge of the image, thus making the corrected image closer to the ideal image.Keywords: image distortion, bilinear interpolation, correction model. Citation: Li J, Su J, Zeng X. A solution method for image distortion correction model based on bilinear interpolation.

Image restoration is the process to eliminate or reduce the image quality degradation in the digital image formation, transmission and recording and its purpose is to process the observed degraded image Keywords: Image Restoration, Ant Colony Algorithm, Genetic AlgorithmCopyright © 2015 Universitas Ahmad Dahlan. All rights reserved. IntroductionImage restoration is to research how to restore the degraded image into real image, or to research how to invert the information obtained into the information related to the real objective [1]. Certain degree of degradation and distortion are inevitable in the formation, transmission, storage, recording and display of the image. Since image quality degradation may be caused in every link of the formation of digital image, in many cases, the image needs to be restored in order to get high-quality digital image [2].In the past decades, domestic and foreign scholars have made extensive and in-depth research in image restoration technology. Many one-dimensional signal processing and estimation theories, including inverse filtering, minimum mean error estimation and Bayesian estimation, have been used in the field of image restoration, thus forming numerous restoration algorithms. In terms of certain specific restoration problems, the scholars usually integrate many ideas and methods [3]. With the continuous development of signal processing theories, many new processing ideas and restoration methods keep emerging. In drastic contrast with the typical mathematical programming principles, some bionic intelligent optimization algorithms such as ant colony algorithm, genetic algorithm, artificial neural network technology, artificial immune algorithm and swarm intelligence algorithm, have been raised and studied by simulating the natural eco-system to seek the complicated optimization problems. These algorithms have greatly enriched modern optimization technology and provided feasible solutions to those optimization problems which are difficult to be handled by traditional optimization technology. Ant colony algorithm is a heuristic bionic evolutionary system based on population. By adopting distributed plus-feedback parallelization, this algorithm is easy to combine with other methods and it also has strong robustness, however, this algorithm requires long search time and it is easy to result in pre-mature and stagnation behaviors, slowing its convergence rate. On the other hand, genetic algorithm is a randomized adaptive search algorithm by referring to the natural selection and natural genetic mechanism and it can compute the non-linear multi-dimensional data space in a quick and effective manner. Therefore, the integration of these two techniques or algorithms can eliminate their own shortcomings and setbacks and utilize their own advantages in the image restoration [4,5]. This paper firstly summarizes the theory of image restoration systematically and analyzes the basic principle of ant colony algorithm. Then, it focuses on the research of the

How to effectively store and transmit such multi-media files as image and video has become a research hotspot. The traditional compression algorithms have a relatively low compression ratio and bad quality of decoded image, at present, the fractal image compression method with a higher compression Keywords: multi-wavelet, fractal theory, image compressionCopyright © 2015 Universitas Ahmad Dahlan. All rights reserved. IntroductionThe image compression technology is a technique to use as few bits as possible to express the image signal from the information source to loweras much resource consumption such as the frequency bandwidth occupied by the image data, the storage space and the transmission time as possible for the sake of the transmission and storage of image signal. In fact, there exists strong correlation between the image pixels and such correlation has brought plenty of redundant information to the image, which makes image compression possible [1]. As a new image compression algorithm developed in the past decade, fractal image compression method attaches great importance to digging the self-similarity in most images and realizes the coding of an image with complicated visual characteristics on the surface via limited coefficients by using the iterated function system and some simple iteration rules. By using these rules, the decoder can realize the iterative decoding of the original image, therefore, the fractal image compression algorithm can achieve a highercompression ratio than other image compression algorithms [2].However, the fractal image compression algorithm still has many problems in both theory and application. For example, during the compression, the computation is too complicated, the compression time is too long, the convergence process is difficult to predict and control and there is block effect in the high compression ratio. The biggest problem of the basic fractal image compression algorithm is that its high compression ratio is at the cost of the huge coding time. It requires global search on all the domain blocks for every R block to search for the optimal matching domain block, therefore, the coding phase demands much computation time [3]. It usually takes hours to code a common 256x256 image, which greatly affects the practicability of fractal image compression, therefore, numerous improved algorithms are trying to find a quick way to accelerate the coding speed,and nevertheless, the increased coding speed comes together with the decreased image-reproduction quality. Tosurmount the shortcomings of the traditional fractal image compression algorithms, this paper make some research on the coding method integrating fractal and wavelet transform. In essence, wavelet transform is to analyze the signal in multi-resolution or multi-scaling, which is very suitable for the logarithmic characteristics of human-eye visual system on the frequency perception.