New Edge-directed Interpolation Research Articles

Research on an SICM Scanning Image Resolution Enhancement Algorithm.

Scanning ion conductance microscopy (SICM) enables the non-invasive three-dimensional imaging of live cells and other structures in physiological environments. However, when imaging complex samples, SICM faces challenges such as having a low temporal resolution during slow scanning and a reduced signal-to-noise ratio during fast scanning, making it difficult to simultaneously improve both temporal and spatial resolution. To address these issues, this paper proposes an algorithm for enhancing image resolution under high-speed scanning. Firstly, scanning images are preprocessed using a median filtering algorithm to remove the salt-and-pepper noise generated during high-speed scanning. Next, the Canny edge detection algorithm is employed to extract the edges of the image targets. To avoid blurring the edges, the new edge-directed interpolation (NEDI) algorithm is then used to fill the edges, while non-edge areas are filled using bilinear interpolation, thereby enhancing the image resolution. Finally, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to analyze the imaging of articular chondrocytes. The results show that under a scanning speed of 480 nm/ms, the proposed algorithm improves the temporal resolution of imaging by 60% compared to traditional 2× resolution imaging, increases the peak signal-to-noise ratio of the scanning images by 7 dB, and achieves a structural similarity of 0.97. Therefore, the proposed algorithm effectively removes noise during high-speed scanning and improves the SICM scanning imaging resolution, thereby avoiding the reduction in temporal resolution when scanning larger resolution samples and effectively enhancing the performance of SICM scanning imaging.

An Efficient Satellite Image Super Resolution Technique for Shift-Variant Images using Improved New Edge Directed Interpolation

Super resolution is a method that reconstructs a higher resolution image from single captured image or set of captured low resolution images. Super resolution imaging is used for several image processing applications like medical imaging, earth observation systems and surveillance systems. Image interpolation is one of the conventional methods used to enhance the resolution of the image. Basic linear interpolation methods like bilinear, bicubic give the blurred image as a result. Non-linear interpolation methods like New Edge Directed Interpolation (NEDI), Curvature based interpolation, neural network based interpolation enhance the image but has limitations like several artifacts. In this paper, a novel innovative approach is proposed in which using dual tree complex wavelet transform (DT-CWT), low and high frequency sub bands are generated. High frequency sub band images are interpolated using improved NEDI which is NEDI with a circular window and dynamic window. Improved NEDI (INEDI) algorithm proposed in the paper gives better results on high frequency components which lead to high resolution image without artifacts. Inverse DT-CWT is applied on interpolated sub bands to reconstruct high resolution image. Registration is applied on both images and shift adaptable bilinear interpolation is applied which reconstructs image into 4 interpolation factor. The proposed approach is verified for different interpolation factors and for different satellite images. The accuracy of proposed approach is verified by several contrast features. The algorithm proposed in this paper outperforms in comparison to state of the art algorithms.

Satellite image resolution enhancement using discrete wavelet transform and new edge-directed interpolation

An image resolution enhancement approach based on discrete wavelet transform (DWT) and new edge-directed interpolation (NEDI) for degraded satellite images by geometric distortion to correct the errors in image geometry and recover the edge details of directional high-frequency subbands is proposed. The observed image is decomposed into four frequency subbands through DWT, and then the three high-frequency subbands and the observed image are processed with NEDI. To better preserve the edges and remove potential noise in the estimated high-frequency subbands, an adaptive threshold is applied to process the estimated wavelet coefficients. Finally, the enhanced image is reconstructed by applying inverse DWT. Four criteria are introduced, aiming to better assess the overall performance of the proposed approach for different types of satellite images. A public satellite images data set is selected for the validation purpose. The visual and quantitative results show the superiority of the proposed approach over the conventional and state-of-the-art image resolution enhancement techniques.

From coarse- to fine-grained implementation of edge-directed interpolation using a GPU

The new edge-directed interpolation (NEDI) algorithm is non-iterative and orientation-adaptive. It achieves better edge performance in enhancing remote sensing images and natural images than conventional bi-linear and bi-cubic methods. It is also the theoretical foundation of many other complex regression and auto-regression interpolation methods. Although NEDI has impressive performance, its computation complexity is an obstacle to large-scale expansion. Parallel acceleration of NEDI provides strong versatility and extensibility. In this paper, we propose a fine-grained implementation for NEDI using GPU . In the fine-grained approach, we assign calculations of one unknown pixel to 2 × 2, 2 × 4, and 4 × 4 threads. Using NVIDIA TESLA K40C GPU for a case of asynchronous I/O transfer, our GPU optimization efforts using fine-grained NEDI can achieve a speedup of 99.09-fold when considering the I/O transfer time, compared with the original single-threaded C CPU code with the -O2 compiling optimization on Intel core™ i7-920. To demonstrate the effectiveness of our fine-grained scheme, we also compare the fine- and coarse-grained schemes by interpolating a 720p video to 1440p. Adopting the fine-grained scheme, we can achieve real-time display. The fine-grained parallel mode can be expanded to other algorithms based on regression and auto-regression schemes.

Algorithm and VLSI Architecture of Edge-Directed Image Upscaling for 4k Display System

High-quality and cost-efficient image upscaling design is very important for many real-time video processing applications, especially when the display panel resolution reaches ultrahigh definition. Compared with New Edge-Directed Interpolation (NEDI) based implicit edge directional upscaling, explicit methods require less computational resource and more easily reach real-time performance, especially when the required image definition and upscaling ratio are very high. Nevertheless, the investigation of applications of explicit methods in video processing systems remains largely missing arguably because it is commonly believed that explicit edge-directed interpolation tends to introduce unexpected artifacts because of inaccurate detection and hence its image quality is relatively poor. This paper proposes an explicit edge-directed adaptive interpolation method that leverages more sophisticated edge detection and orientation estimation algorithms to avoid misinterpolation, thereby providing similar or even better image quality than those with implicit methods. Targeting the real-time 4K video display system, the proposed edge-directed image upscaling algorithm is further implemented with an efficient very-large-scale integration (VLSI) architecture. The experimental results demonstrate that the proposed interpolation algorithm outperforms previous explicit and implicit edge-directed methods in both objective and subjective tests. The presented VLSI implementation further demonstrates that the maximum output video sequence of the proposed interpolation method can reach 4k × 2k@60 Hz with a reasonable hardware cost.

New Edge-Directed Interpolation Based-Lifting DWT and MSPIHT Algorithm for Image Compression

The amount of image data generated in multimedia applications is ever increasing. The image compression plays vital role in multimedia applications. The ultimate aim of image compression is to reduce storage space without degrading image quality. Compression is required whenever the data handled is huge they may be required to sent or transmitted and also stored. The New Edge Directed Interpolation (NEDI)-based lifting Discrete Wavelet Transfrom (DWT) scheme with modified Set Partitioning In Hierarchical Trees (MSPIHT) algorithm is proposed in this paper. The NEDI algorithm gives good visual quality image particularly at edges. The main objective of this paper is to be preserving the edges while performing image compression which is a challenging task. The NEDI with lifting DWT has achieved 99.18% energy level in the low frequency ranges which has 1.07% higher than 5/3 Wavelet decomposition and 0.94% higher than traditional DWT. To implement this NEDI with Lifting DWT along with MSPIHT algorithm which gives higher Peak Signal to Noise Ratio (PSNR) value and minimum Mean Square Error (MSE) and hence better image quality. The experimental results proved that the proposed method gives better PSNR value (39.40 dB for rate 0.9 bpp without arithmetic coding) and minimum MSE value is 7.4.

Super-Resolution Method Based on Edge Feature for High Resolution Imaging

Super-resolution is recently one of the most attractive research themes. An image interpolation method using co-variance between neighboring pixels, so called New Edge-Directed Interpolation (NEDI), was proposed. It enables to interpolate pixels quantitatively regardless of edge features. However, in estimation of predictive coefficients and determination of NEDI’s local window size, edge features are not made consideration. So, NEDI cannot necessarily satisfy quality of picture. In order to overcome this problem, we propose a new intra-frame super-resolution method that window sizes and configurations are adaptively determined depending on edge strengths and orientations. Experimental simulation shows that the proposed method can interpolate pixels more clearly than NEDI for many kinds of edges.

Improved fast new edge-directed fractional interpolation algorithm

The original New Edge-Directed Interpolation(NEDI) algorithm is of high complexity,difficult for hardware implementation,and the interpolated images may suffer from blurring edges around edge area.To achieve a better subjective quality,an improved NEDI algorithm was proposed in this paper.In the new algorithm,a circular window was adopted,and the interpolation coefficient calculation was calculated only once,which could be reused in interpolating the center-pixels,thus the errors introduced by iterative computation were avoided and the interpolation time was saved.As to non-center pixels,six original neighbors were involved to estimate local covariance characteristics at high resolution.In comparison with the results of bi-cubic interpolation and the traditional NEDI,the experimental results indicate that proposed algorithm can eliminate the sawtooth of the interpolated picture in large-scale,and decrease the computational complexity.

A Low Complexity Switched Method for Enhancement of Spatial Resolution of Images

This paper presents a novel approach of image interpolation based on the switching of new edge directed interpolation (NEDI) and single pass interpolation algorithm ( SPIA ) and switching is based upon the % of edges present in the blocks of the image. The switching of this interpolation algorithm is block based instead of image based or pixel based. Imperially we found that NEDI methods is better applicable for smoother images (variation among the pixels is less) while SPIA method works better on detailed images (more variation among the pixels), because of the type of pixels used in the process interpolation. So, a hybrid scheme of combining NEDI method and SPIA method is used for better prediction of HR image. The proposed algorithm produces the better results for different varieties of images in terms of both PSNR measurement and subjective visual quality with low computational complexity as compare to recently developed interpolation algorithms.

Fast image interpolation using the bilateral filter

In this study, the authors propose a new image interpolation technique using the bilateral filter to estimate the unknown high-resolution pixels. Compared with the least-squares estimation, a small-kernel bilateral filter has the advantages of fast computation and stability. The range distance of the bilateral filter is estimated using a novel maximum a posterior estimation, in order to consider both the diagonal and vertical–horizontal correlations. For the consideration of global consistency, the pixel-based soft-decision estimation (SAI) is proposed to constrain the consistency of edge statistic within a local window. Experimental results show that the interpolated images using the proposed algorithm give an average of 0.462, 0.413, 0.532 and 0.036 dB peak signal-to-noise ratio (PSNR) improvement compared with that using the bicubic interpolation, linear minimum mean squares error estimation, new edge-directed interpolation (NEDI) and SAI respectively. The subjective quality agrees with the PSNR as well. More importantly, the proposed algorithm is fast and it requires around 1/60 computational cost of the SAI.

Robust edge-directed interpolation of magnetic resonance images

Image interpolation is intrinsically a severely under-determined inverse problem. Traditional non-adaptive interpolation methods do not account for local image statistics around the edges of image structures. In practice, this results in artifacts such as jagged edges, blurring and/or edge halos. To overcome this shortcoming, edge-directed interpolation has been introduced in different forms. One variant, new edge-directed interpolation (NEDI), has successfully exploited the ‘geometric duality’ that links the low-resolution image to its corresponding high-resolution image. It has been demonstrated that for scalar images, NEDI is able to produce better results than non-adaptive traditional methods, both visually and quantitatively. In this work, we return to the root of NEDI as a least-squares estimation method of neighborhood patterns and propose a robust scheme to improve it. The improvement is twofold: firstly, a robust least-squares technique is used to improve NEDI's performance to outliers and noise; secondly, the NEDI algorithm is extended with the recently proposed non-local mean estimation scheme. Moreover, the edge-directed concept is applied to the interpolation of multi-valued diffusion-weighted images. The framework is tested on phantom scalar images and real diffusion images, and is shown to achieve better results than the non-adaptive methods as well as NEDI, in terms of visual quality as well as quantitative measures.

Real-Time Artifact-Free Image Upscaling

The problem of creating artifact-free upscaled images appearing sharp and natural to the human observer is probably more interesting and less trivial than it may appear. The solution to the problem, often referred to also as "single-image super-resolution," is related both to the statistical relationship between low-resolution and high-resolution image sampling and to the human perception of image quality. In many practical applications, simple linear or cubic interpolation algorithms are applied for this task, but the results obtained are not really satisfactory, being affected by relevant artifacts like blurring and jaggies. Several methods have been proposed to obtain better results, involving simple heuristics, edge modeling, or statistical learning. The most powerful ones, however, present a high computational complexity and are not suitable for real-time applications, while fast methods, even if edge adaptive, are not able to provide artifacts-free images. In this paper, we describe a new upscaling method (iterative curvature-based interpolation) based on a two-step grid filling and an iterative correction of the interpolated pixels obtained by minimizing an objective function depending on the second-order directional derivatives of the image intensity. We show that the constraints used to derive the function are related with those applied in another well-known interpolation method, providing good results but computationally heavy (i.e., new edge-directed interpolation (NEDI). The high quality of the images enlarged with the new method is demonstrated with objective and subjective tests, while the computation time is reduced of one to two orders of magnitude with respect to NEDI so that we were able, using a graphics processing unit implementation based on the nVidia Compute Unified Device Architecture technology, to obtain real-time performances.