رسم عثمانی میں غیر موجود اور صحیح سند سے ثابت قراءات کا حکم

Some of the variant readings of the Holy Quran having a sound chain of narration are not included in the Uthmanic Maṣāḥif (Codices). Hence, following three probabilities can be deduced about these readings; First: Those were abrogated in ʿArḍah Akhīrah (the last revision). Second: Those might be among those explanatory notes of the Holy Text by Prophet Muhammad (SWA) that were erroneously written by a few companions within the actual text of Quran considering them a part of the Quran. Third: Those may belong to such Aḥruf (readings) that were authentically transmitted from the Messenger of Allah (SWA) but, they were not mentioned in the orthography of the Uthmanic Maṣāḥif by the compilers due to any possible reason. To us, if we come across any authentically narrated recitation of the senior Qurrāʾ companions that seems contrary to the orthography of the Uthmanic Maṣāḥif and there is no proof of their being from the second category, then, it is better to consider them from the third category instead of the first one.

Development of New Image Fusion Techniques

Image fusion techniques merge the complementary information of several images (multi-focus, multi-exposure and multi-modal). Each of these scenarios poses different challenges for image fusion techniques, which are being extensively researched. However, most of these works assume that source images are preregistered, which is a less practical scenario. Both registered and unregistered image fusion algorithms are considered in this thesis. The registration involves the geometrical / spatial alignment of source images taken using different sensors or a sensor in different operating conditions. This research is concerned with the reliable fusion schemes of several scenario images (including muti-focus, Infra Red (IR) and visible, Computed Tomography (CT) and Magnetic Resonance (MR), and multi-exposure images) demonstrating high quality fused results without loss of useful information. The first scheme is a textural registration based multi-focus scheme involving the Gabor filtering (with specific frequency and orientation) for extracting texture features from the images. The filtered images are aligned/registered using affine transformation. Noise and blur play an important role in image fusion and need to be classified and treated for quality image fusion. The next two fusion schemes deal with multi-exposure noisy (real and synthetic both) and blur images. In the first algorithm, the noisy, blurry and clean images are classified using Laplacian filter and histogram spread. The noise is reduced in the frequency domain. Heavy weights are assigned to noise free pixels and the blur images are passed through the Wiener filter. In the second algorithm, a noise resistant image fusion scheme for multi-exposure sensors using color dissimilarity (for motion detection and removal), median and noise maps is proposed. A well exposed image is obtained as a result of weighted average of multi-exposure source images. Higher valued weights are assigned to pixels containing low values of noises, high values of color dissimilarity and median maps. The next work (two schemes) involve pre-registered visible and IR images. In the first one, a three stage image fusion scheme using Genetic Algorithm (GA) is presented. In the first stage, it segments the image into homogeneous regions and generates segmentation maps. In the second stage, the segmentation maps are combined by an adaptive weight adjustment procedure. The third stage fuses the input images and segmentation maps via GA based multi- objective optimization strategy. The second image fusion scheme uses Un-Decimated Dual Tree Complex Wavelet Transform (UDTCWT) for astronomical images. The UDTCWT reduces noise effects and improves object classification due to its inherited shift invariance property. Local standard deviation and distance transforms are used to extract useful information, especially small objects. In the medical (CT and MR) image fusion scheme, the source images are contrast enhanced using histogram equalization. It is a sparse decomposition based fusion technique that uses the dictionary learnt from input images and k-mean singular value decomposition algorithm. The scheme splits CT and MR images into texture and gradient images. The texture decomposition improves the overall performance of the sparse representation based fusion. The quantitative analysis performed using mutual information, structural similarity measure and edge dependent based performance metrics, yields improved results for proposed schemes, as compared to existing schemes.